TY  - JOUR
T1  - Predictive Modeling of Diabetes Status Based on Vitamin D Levels and Clinical Parameters: A Machine Learning Investigation
TT  - Predictive Modeling of Diabetes Status Based on Vitamin D Levels and Clinical Parameters: A Machine Learning Investigation
AU  - Balcıoğlu, Yavuz Selim
AU  - Sertbakan, Kübra
AU  - Sezen, Bülent
AU  - Garip, Tayfun
AU  - Aslanbaş, Çağlayan
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.34248/bsengineering.1649690
JF  - Black Sea Journal of Engineering and Science
JO  - BSJ Eng. Sci.
PB  - Karyay Karadeniz Yayımcılık Ve Organizasyon Ticaret Limited Şirketi
WT  - DergiPark
SN  - 2619-8991
SP  - 1985
EP  - 1997
VL  - 8
IS  - 6
LA  - en
AB  - Vitamin D deficiency has been associated with impaired glucose metabolism, but its predictive value for diabetes status remains incompletely characterized. We applied machine learning methodologies to investigate this relationship and develop predictive models based on vitamin D levels and clinical parameters. This cross-sectional study analyzed data from 817 patients with concurrent measurements of 25-hydroxyvitamin D and HbA1c. Patients were classified as having normal glucose metabolism (HbA1c
KW  - Vitamin D deficiency
KW  - Diabetes mellitus
KW  - HbA1c
KW  - Machine learning
KW  - Predictive modeling
N2  - Vitamin D deficiency has been associated with impaired glucose metabolism, but its predictive value for diabetes status remains incompletely characterized. We applied machine learning methodologies to investigate this relationship and develop predictive models based on vitamin D levels and clinical parameters. This cross-sectional study analyzed data from 817 patients with concurrent measurements of 25-hydroxyvitamin D and HbA1c. Patients were classified as having normal glucose metabolism (HbA1c
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UR  - https://doi.org/10.34248/bsengineering.1649690
L1  - https://dergipark.org.tr/en/download/article-file/4653600
ER  -